This thesis investigates the effect choice options in e-commerce applications have on consumers’ decision making. Previous research showed that a large number of options
can affect consumers negatively. However, the conditions for such choice overload are unclear. After reviewing the existing research, the amount of information (entropy)
contained in a choice set and individual differences were determined as possible influencing factors in an online environment. In a choice experiment, choice sets with
varying information loads and an assessment of the Big Five personality traits were used to test the impact of the two identified factors on choice avoidance behavior. Results from chi-square-tests and a logistic regression model suggest choice overload but without entropy having an effect. A logistic regression model revealed that extraverted consumers are easier overloaded. A low Neuroticism score was found to be related to less occurrence of a too-much-choice-effect. Consumers with a high Openness score on the other hand choose one of the presented options more often and were therefore less often
overwhelmed by the assortment. An interaction effect between personality and the amount of entropy was not found. These findings extend the research on choice overload and offer valuable input for marketers targeting consumers online.

Smart mobility is the future of transportation services in Germany. The implementation and management of smart mobility is impossible without using big data. At the present time,the analysis of big data in Germany is not fully implemented due to existing challenges. The purpose of this research project is to forecast the impact of big data on smart mobility in Germany with the use of scenario planning. In order to receive the most actual scenarios, the input factors were designed in accordance with extensive literature research, and then ratios between all specifications of input factors were compared and evaluated. Thus four unique scenarios were selected for further detailed interpretation to suggest possible influences of big data on smart mobility in Germany